Reviewing videos from medical procedures is a tedious work that requires concentration for extended hours and
usually screens thousands of frames to find only a few positive cases that indicate probable presence of disease.
Computational classification algorithms are sought to automate the reviewing process. The class imbalance
problem becomes challenging when the learning process is driven by relative few minority class samples. The
learning algorithms using imbalanced data sets generally result in large number of false negatives. In this article,
we present an efficient rebalancing method for finding video frames that contain bleeding lesions. The majority
class generally has clusters of data within them. Here we cluster the majority class and under-sample the each
cluster based on its variance so that useful examples would not be lost during the under-sampling process. The
balance of bleeding to non-bleeding frames is restored by the proposed cluster-based under-sampling and oversampling
using Synthetic Minority Over-sampling Technique (SMOTE). Experiments were conducted using
synthetic data and videos manually annotated by medical specialists for obscure bleeding detection. Our method
achieved a high average sensitivity and specificity.

The evaluation of automated classifiers in computer-aided diagnosis of medical images often involves a training dataset for classifier design and a test dataset for performance estimation in terms of, e.g., area under the receiver operating characteristic (ROC) curve, or AUC. The traditional approach to assess the uncertainty of the estimated AUC only considers the finite testing set as the source of variability. However, a finite training set is also a random sample and the AUC varies with varying training sets. We categorize the assessment of classifiers into three levels and provide analytical expressions for the variance of the estimated AUC at each level: (1) training treated as a fixed effect, the estimated performance generalizable only to the population of testing sets; (2) training treated as a random effect, the estimated performance generalizable to both the population of training sets and the population of testing sets; (3) training treated as a random effect, performance averaged over training sets generalizable to both the population of training sets and the population of testing sets. The two sources of variability - training and testing - in automated classifiers are analogous to readers and cases in the multi-reader multi-case (MRMC) ROC paradigm in reader studies. We show the one-to-one analogy between the automated classifiers and human readers at these three levels as well as the practical difference in estimating their performance, especially regarding variance.

Ultrasonography is a valuable technique for diagnosing breast cancer. Computer-aided tumor detection and
segmentation in ultrasound images can reduce labor cost and streamline clinic workflows. In this paper, we
propose a fully automatic system to detect and segment breast tumors in 2D ultrasound images. Our system,
based on database-guided techniques, learns the knowledge of breast tumor appearance exemplified by expert
annotations. For tumor detection, we train a classifier to discriminate between tumors and their background.
For tumor segmentation, we propose a discriminative graph cut approach, where both the data fidelity and
compatibility functions are learned discriminatively. The performance of the proposed algorithms is demonstrated
on a large set of 347 images, achieving a mean contour-to-contour error of 3.75 pixels with about 4.33 seconds.

We have previously reported an interactive information-theoretic CADe (IT-CADe) system for the detection of masses
in screening mammograms. The system operates in either traditional static mode or in interactive mode whenever the
user requests a second opinion. In this study we report preliminary investigation of a new paradigm of clinical
integration, guided by the user's eye-gazing and reporting patterns. An observer study was conducted in which 6
radiologists evaluated 20 mammographic cases while wearing a head-mounted eye-tracking device. For each radiologistreported
location, eye-gazing data were collected. Image locations that attracted prolonged dwelling (>1000msec) but
were not reported were also recorded. Fixed size regions of interest (ROIs) were extracted around all above locations and
analyzed using the IT-CADe system. Preliminary analysis showed that IT-CADe correctly confirmed 100% of reported
true mass locations while eliminating 12.5% of the reported false positive locations. For unreported locations that
attracted long dwelling, IT-CADe identified 4/6 false negative errors (i.e., errors of decision) while overcalling 8/84 TN
decisions. Finally, for missed true masses that attracted short (i.e., errors of recognition) or no dwelling at all (i.e., errors
of search), IT-CADe detected 5/8 of them. These results suggest that IT-CADe customization to the user's eye-gazing
and reporting pattern could potentially help delineate the various sources of diagnostic error (search, recognition,
decision) for each individual user and provide targeted decision support, thus improving the human-CAD synergy.

A learning-based approach integrating the use of pixel level statistical modeling and spiculation detection is
presented for the segmentation of mammographic masses with ill-defined margins and spiculations. The algorithm
involves a multi-phase pixel-level classification, using a comprehensive group of regional features, to generate a
pixel level mass-conditional probability map (PM). Then, mass candidate along with background clutters are
extracted from the PM by integrating the prior knowledge of shape and location of masses. A multi-scale steerable
ridge detection algorithm is employed to detect spiculations. Finally, all the object level findings, including mass
candidate, detected spiculations, and clutters, along with the PM are integrated by graph cuts to generate the
final segmentation mask. The method was tested on 54 masses (51 malignant and 3 benign), all with ill-defined
margins and irregular shape or spiculations. The ground truth delineations were provided by five experienced
radiologists. Area overlap ratio of 0.766 (±0.144) and 0.642 (±0.173) were obtained for segmenting the whole mass
and only the margin portion, respectively. Williams index of area and contour based measurements indicated
that segmentation results of the algorithm well agreed with the radiologists' delineation. Most importantly, the
proposed approach is capable of including mass margin and its extension which are considered as key features
for breast lesion analyses.

This paper presents methods for the detection of architectural distortion in mammograms of interval-cancer cases
taken prior to the diagnosis of breast cancer, using Gabor filters, phase portrait analysis, fractal dimension (FD),
and analysis of the angular spread of power in the Fourier spectrum. In the estimation of FD using the Fourier
power spectrum, only the distribution of power over radial frequency is considered; the information regarding
the angular spread of power is ignored. In this study, the angular spread of power in the Fourier spectrum is
used to generate features for the detection of spiculated patterns related to architectural distortion. Using Gabor
filters and phase portrait analysis, a total of 4224 regions of interest (ROIs) were automatically obtained from
106 prior mammograms of 56 interval-cancer cases, including 301 ROIs related to architectural distortion, and
from 52 mammograms of 13 normal cases. For each ROI, the FD and measures of the angular spread of power
were computed. Feature selection was performed using stepwise logistic regression. The best result achieved,
in terms of the area under the receiver operating characteristic curve, is 0.75 ± 0.02 with an artificial neural
network including radial basis functions. Analysis of the performance of the methods with free-response receiver
operating characteristics indicated a sensitivity of 0.82 at 7.7 false positives per image.

We performed a study to compare methods for volumetric breast density estimation in digital mammography (DM) and
magnetic resonance imaging (MRI) for a high-risk population of women. DM and MRI images of the unaffected breast
from 32 women with recently detected abnormalities and/or previously diagnosed breast cancer (age range 31-78 yrs,
mean 50.3 yrs) were retrospectively analyzed. DM images were analyzed using QuantraTM (Hologic Inc). The MRI
images were analyzed using a fuzzy-C-means segmentation algorithm on the T1 map. Both methods were compared to
Cumulus (Univ. Toronto). Volumetric breast density estimates from DM and MRI are highly correlated (r=0.90,
p≤0.001). The correlation between the volumetric and the area-based density measures is lower and depends on the
training background of the Cumulus software user (r=0.73-84, p≤0.001). In terms of absolute values, MRI provides the
lowest volumetric estimates (mean=14.63%), followed by the DM volumetric (mean=22.72%) and area-based measures
(mean=29.35%). The MRI estimates of the fibroglandular volume are statistically significantly lower than the DM
estimates for women with very low-density breasts (p≤0.001). We attribute these differences to potential partial volume
effects in MRI and differences in the computational aspects of the image analysis methods in MRI and DM. The good
correlation between the volumetric and the area-based measures, shown to correlate with breast cancer risk, suggests
that both DM and MRI volumetric breast density measures can aid in breast cancer risk assessment. Further work is
underway to fully-investigate the association between volumetric breast density measures and breast cancer risk.

We are investigating the feasibility of improving breast cancer risk prediction by computerized mammographic
parenchymal pattern (MPP) analysis. A case-control study was conducted to investigate the association of the MPP
measures with breast cancer risk. The case group included 168 contralateral CC-view mammograms of breast cancer
patients dated at least one year prior to cancer diagnosis, and the control group included 522 CC-view mammograms
from one breast of normal subjects. We extracted and compared four types of statistical texture feature spaces that
included run length statistics and region size statistics (RLS/RSS) features, spatial gray level dependence (SGLD)
features, gray level difference statistics (GLDS) features, and the feature space combining these three types of texture
features. A linear discriminant analysis (LDA) classifier with stepwise feature selection was trained and tested with
leave-one-case-out resampling to evaluate whether the breast parenchyma of future cancer patients could be
distinguished from those of normal subjects in each feature space. The areas under ROC curves (Az) were 0.71, 0.72,
0.71 and 0.76 for the four feature spaces, respectively. The Az obtained from the combined feature space was
significantly (p<0.05) higher than those from the individual feature spaces. Odd ratios (OR) were used to assess the
association between breast cancer risk and four categories of MPP measures: <0.1 (C1), 0.1-0.15 (C2), 0.15-0.2 (C3),
and >0.2 (C4) while patient age was treated as a confounding factor. The adjusted ORs of breast cancer for C2, C3 and
C4 were 3.23, 7.77 and 25.43, respectively. The preliminary result indicated that our proposed computerized MPP
measures were strongly associated with breast cancer risk.

A large number of false positives (FPs) generated by computer-aided detection schemes is likely to distract radiologists'
attention and decreases their interpretation efficiency. Therefore, it is desirable to reduce FPs as many as possible to
increase the detection specificity while maintaining the high detection sensitivity. In this paper, several features are
extracted from the projected images of each initial polyp candidate to differentiate FPs from true positives. These
features demonstrate the potential to exclude different types of FPs, like haustral folds, rectal tubes and residue stool by
an evaluation using a database of 325 patient studies (from two different institutions) which includes 556 scans at supine
and/or prone positions with 347 polyps and masses sized from 5 to 60 mm. For comparison purpose, several wellestablished
features are used to generate a baseline reference. At the by-polyp detection sensitivity level of 96% (no loss
of detection sensitivity), the number of FPs per scan is 7.8 by the baseline and 3.75 if the new projection features are
added, which is a reduction of 51.9% FPs from the baseline.

Partial volume effect and inhomogeneity are two major causes of artifacts in electronic cleansing (EC) for non-cathartic
CT colonography (CTC). Our purpose was to develop a novel method of EC for non-cathartic dual-energy CTC (DECTC)
using a subvoxel multi-spectral material classifier and a regional material decomposition method for
differentiation of residual fecal materials from colonic soft-tissue structures. In this study, an anthropomorphic colon
phantom, which was filled with a mixture of aqueous fiber (psyllium), ground foodstuff (cereal), and non-ionic iodinated
agent (Omnipaque iohexol, GE Healthcare, Milwaukee, WI), was scanned by a dual-energy CT scanner (SOMATON,
Siemens) with two photon energies: 80 kVp and 140 kVp. The DE-CTC images were subjected to a dual-energy EC
(DE-EC) scheme, in which a multi-spectral material classifier was used to compute the fraction of each material within
one voxel by an expectation-maximization (EM) algorithm. This was followed by a regional material segmentation
method for identifying of homogeneous sub-regions (tiles) as fecal materials from other tissue types. The results were
compared with the structural-analysis cleansing (SA-EC) method based upon the CTC images of native phantom without
fillings. The mean cleansing ratio of the DE-EC scheme was 96.57±1.21% compared to 76.3±5.56% of the SA-EC
scheme. The soft-tissue preservation ratio of the DE-EC scheme was 97.05%±0.64% compared to 99.25±0.77% of the
SA-EC scheme.

Predicting the malignancy of colonic polyps is a difficult problem and in general requires an invasive polypectomy
procedure. We present a less-invasive and automated method to predict the histology of colonic polyps under computed
tomographic colonography (CTC) using the content-based image retrieval (CBIR) paradigm. For the purpose of
simplification, polyps annotated as hyperplastic or "other benign" were classified as benign polyps (BP) and the rest
(adenomas and cancers) were classified as malignant polyps (MP). The CBIR uses numerical feature vectors generated
from our CTC computer aided detection (CTC-CAD) system to describe the polyps. These features relate to physical and
visual characteristics of the polyp. A representative database of CTC-CAD polyp images is created. Query polyps are
matched with those in the database and the results are ranked based on the similarity to the query. Polyps with a majority
of representative MPs in their result set are predicted to be malignant and similarly those with a majority of BPs in their
results are benign. For evaluation, the system is compared to the typical optical colonoscopy (OC) size based
classification. Using receiver operating curve (ROC) analysis, we show our system is sufficiently better than the OC size
method.

Computed tomographic colonography (CTC) combined with a computer aided detection system has the potential for
improving colonic polyp detection and increasing the use of CTC for colon cancer screening. In the clinical use of CTC,
a true colonic polyp will be confirmed with high confidence if a radiologist can find it on both the supine and prone
scans. To assist radiologists in CTC reading, we propose a new method for matching polyp findings on the supine and
prone scans. The method performs a colon registration using four automatically identified anatomical salient points and
correlation optimized warping (COW) of colon centerline features. We first exclude false positive detections using
prediction information from a support vector machine (SVM) classifier committee to reduce initial false positive pairs.
Then each remaining CAD detection is mapped to the other scan using COW technique applied to the distance along the
centerline in each colon. In the last step, a new SVM classifier is applied to the candidate pair dataset to find true polyp
pairs between supine and prone scans. Experimental results show that our method can improve the sensitivity to 0.87 at 4
false positive pairs per patient compared with 0.72 for a competing method that uses the normalized distance along the
colon centerline (p<0.01).

For pharmacokinetic (PK) analysis of Dynamic Contrast Enhanced (DCE) MRI the arterial input function
needs to be estimated. Previously, we demonstrated that PK parameters have a significant better discriminative
performance when per patient reference tissue was used, but required manual annotation of reference tissue. In
this study we propose a fully automated reference tissue segmentation method that tackles this limitation. The
method was tested with our Computer Aided Diagnosis (CADx) system to study the effect on the discriminating
performance for differentiating prostate cancer from benign areas in the peripheral zone (PZ).
The proposed method automatically segments normal PZ tissue from DCE derived data. First, the bladder
is segmented in the start-to-enhance map using the Otsu histogram threshold selection method. Second, the
prostate is detected by applying a multi-scale Hessian filter to the relative enhancement map. Third, normal
PZ tissue was segmented by threshold and morphological operators. The resulting segmentation was used as
reference tissue to estimate the PK parameters. In 39 consecutive patients carcinoma, benign and normal tissue
were annotated on MR images by a radiologist and a researcher using whole mount step-section histopathology
as reference. PK parameters were computed for each ROI. Features were extracted from the set of ROIs using
percentiles to train a support vector machine that was used as classifier. Prospective performance was estimated
by means of leave-one-patient-out cross validation. A bootstrap resampling approach with 10,000 iterations was
used for estimating the bootstrap mean AUCs and 95% confidence intervals.
In total 42 malignant, 29 benign and 37 normal regions were annotated. For all patients, normal PZ was
successfully segmented. The diagnostic accuracy obtained for differentiating malignant from benign lesions using
a conventional general patient plasma profile showed an accuracy of 0.64 (0.53-0.74). Using the automated
per-patient calibration method the diagnostic performance improved significantly to 0.76 (0.67-0.86, p=0.017) ,
whereas the manual per-patient calibration showed a diagnostic performance of 0.79 (0.70-0.89, p=0.01).
In conclusion, the results show that an automated per-patient reference tissue PK model is feasible. A
significantly better discriminating performance compared to the conventional general calibration was obtained
and the diagnostic accuracy is similar to using manual per-patient calibration.

Pathological myopia is the seventh leading cause of blindness. We introduce a framework based on PAMELA
(PAthological Myopia dEtection through peripapilLary Atrophy) for the detection of pathological myopia from fundus
images. The framework consists of a pre-processing stage which extracts a region of interest centered on the optic disc.
Subsequently, three analysis modules focus on detecting specific visual indicators. The optic disc tilt ratio module gives
a measure of the axial elongation of the eye through inference from the deformation of the optic disc. In the texturebased
ROI assessment module, contextual knowledge is used to demarcate the ROI into four distinct, clinically-relevant
zones in which information from an entropy transform of the ROI is analyzed and metrics generated. In particular, the
preferential appearance of peripapillary atrophy (PPA) in the temporal zone compared to the nasal zone is utilized by
calculating ratios of the metrics. The PPA detection module obtains an outer boundary through a level-set method, and
subtracts this region against the optic disc boundary. Temporal and nasal zones are obtained from the remnants to
generate associated hue and color values. The outputs of the three modules are used as in a SVM model to determine the
presence of pathological myopia in a retinal fundus image. Using images from the Singapore Eye Research Institute, the
proposed framework reported an optimized accuracy of 90% and a sensitivity and specificity of 0.85 and 0.95
respectively, indicating promise for the use of the proposed system as a screening tool for pathological myopia.

Diabetic retinopathy (DR) is one of the leading causes of blindness among adult Americans. Automatic
methods for detection of the disease have been developed in recent years, most of them addressing the
segmentation of bright and red lesions. In this paper we present an automatic DR screening system that does
approach the problem through the segmentation of features. The algorithm determines non-diseased retinal
images from those with pathology based on textural features obtained using multiscale Amplitude
Modulation-Frequency Modulation (AM-FM) decompositions. The decomposition is represented as features
that are the inputs to a classifier. The algorithm achieves 0.88 area under the ROC curve (AROC) for a set of
280 images from the MESSIDOR database. The algorithm is then used to analyze the effects of image
compression and degradation, which will be present in most actual clinical or screening environments.
Results show that the algorithm is insensitive to illumination variations, but high rates of compression and
large blurring effects degrade its performance.

A lower ratio between the width of the arteries and veins (Arteriolar-to-Venular diameter Ratio, AVR) on the
retina, is well established to be predictive of stroke and other cardiovascular events in adults, as well as an
increased risk of retinopathy of prematurity in premature infants. This work presents an automatic method that
detects the location of the optic disc, determines the appropriate region of interest (ROI), classifies the vessels
in the ROI into arteries and veins, measures their widths and calculates the AVR. After vessel segmentation
and vessel width determination the optic disc is located and the system eliminates all vessels outside the AVR
measurement ROI. The remaining vessels are thinned, vessel crossing and bifurcation points are removed leaving
a set of vessel segments containing centerline pixels. Features are extracted from each centerline pixel that are
used to assign them a soft label indicating the likelihood the pixel is part of a vein. As all centerline pixels
in a connected segment should be the same type, the median soft label is assigned to each centerline pixel in
the segment. Next artery vein pairs are matched using an iterative algorithm and the widths of the vessels is
used to calculate the AVR. We train and test the algorithm using a set of 25 high resolution digital color fundus
photographs a reference standard that indicates for the major vessels in the images whether they are an artery or
a vein. We compared the AVR values produced by our system with those determined using a computer assisted
method in 15 high resolution digital color fundus photographs and obtained a correlation coefficient of 0.881.

Abnormalities of retinal vasculatures can indicate health conditions in the body, such as the high blood pressure and
diabetes. Providing automatically determined width ratio of arteries and veins (A/V ratio) on retinal fundus images may
help physicians in the diagnosis of hypertensive retinopathy, which may cause blindness. The purpose of this study was
to detect major retinal vessels and classify them into arteries and veins for the determination of A/V ratio. Images used in
this study were obtained from DRIVE database, which consists of 20 cases each for training and testing vessel detection
algorithms. Starting with the reference standard of vasculature segmentation provided in the database, major arteries and
veins each in the upper and lower temporal regions were manually selected for establishing the gold standard. We
applied the black top-hat transformation and double-ring filter to detect retinal blood vessels. From the extracted vessels,
large vessels extending from the optic disc to temporal regions were selected as target vessels for calculation of A/V
ratio. Image features were extracted from the vessel segments from quarter-disc to one disc diameter from the edge of
optic discs. The target segments in the training cases were classified into arteries and veins by using the linear
discriminant analysis, and the selected parameters were applied to those in the test cases. Out of 40 pairs, 30 pairs (75%)
of arteries and veins in the 20 test cases were correctly classified. The result can be used for the automated calculation of
A/V ratio.

The mapping of genotype to the phenotype of age-related macular degeneration (AMD) is expected to improve the
diagnosis and treatment of the disease in a near future. In this study, we focused on the first step to discover this
mapping: we identified visual patterns related to AMD which seem to be controlled by genetic factors, without explicitly
relating them to the genes. For this purpose, we used a dataset of eye fundus photographs from 74 twin pairs, either
monozygotic twins, who have the same genotype, or dizygotic twins, whose genes responsible for AMD are less likely to
be identical. If we are able to differentiate monozygotic twins from dizygotic twins, based on a given visual pattern, then
this pattern is likely to be controlled by genetic factors. The main visible consequence of AMD is the apparition of
drusen between the retinal pigment epithelium and Bruch's membrane. We developed two automated drusen detectors
based on the wavelet transform: a shape-based detector for hard drusen, and a texture- and color- based detector for soft
drusen. Forty visual features were evaluated at the location of the automatically detected drusen. These features
characterize the texture, the shape, the color, the spatial distribution, or the amount of drusen. A distance measure
between twin pairs was defined for each visual feature; a smaller distance should be measured between monozygotic
twins for visual features controlled by genetic factors. The predictions of several visual features (75.7% accuracy) are
comparable or better than the predictions of human experts.

In this paper we present a system for fast and accurate detection of anatomical structures (calipers) in M-mode images.
The task is challenging because of dramatic variations in their appearances. We propose to solve the problem in a
progressive manner, which ensures both robustness and efficiency. It first obtains rough caliper localization using the
intensity profile image. Then run a constrained search for accurate caliper positions. Markov Random Field (MRF) and
warping image detectors are used for jointly considering appearance information and the geometric relationship between
calipers. Extensive experiments show that our system achieves more accurate results and uses less time in comparison
with previously reported work.

This work presents a system for automatic coronary calcium scoring and cardiovascular risk stratification in
thoracic CT scans.
Data was collected from a Dutch-Belgian lung cancer screening trial. In 121 low-dose, non-ECG synchronized,
non-contrast enhanced thoracic CT scans an expert scored coronary calcifications manually. A key element of
the proposed algorithm is that the approximate position of the coronary arteries was inferred with a probabilistic
coronary calcium atlas. This atlas was created with atlas-based segmentation from 51 scans and their manually
identified calcifications, and was registered to each unseen test scan. In the test scans all objects with density
above 130 HU were considered candidates that could represent coronary calcifications. A statistical pattern
recognition system was designed to classify these candidates using features that encode their spatial position
relative to the inferred position of the coronaries obtained from the atlas registration. In addition, size and
texture features were computed for all candidates. Two consecutive classifiers were used to label each candidate.
The system was trained with 35 and tested with another 35 scans. The detected calcifications were quantified
and cardiovascular risk was determined for each subject.
The system detected 71% of coronary calcifications with an average of 0.9 false positive objects per scan.
Cardiovascular risk category was correctly assigned to 29 out of 35 subjects (83%). Five scans (14%) were one
category off, and only one scan (3%) was two categories off.
We conclude that automatic assessment of the cardiovascular risk from low-dose, non-ECG synchronized
thoracic CT scans appears feasible.

We developed a hybrid CPU-GPU framework enabling semi-automated segmentation of abdominal aortic aneurysm
(AAA) on Computed Tomography Angiography (CTA) examinations. AAA maximal diameter (D-max) and volume
measurements and their progression between 2 examinations can be generated by this software improving patient followup.
In order to improve the workflow efficiency some segmentation tasks were implemented and executed on the
graphics processing unit (GPU). A GPU based algorithm is used to automatically segment the lumen of the aneurysm
within short computing time. In a second step, the user interacted with the software to validate the boundaries of the
intra-luminal thrombus (ILT) on GPU-based curved image reformation. Automatic computation of D-max and volume
were performed on the 3D AAA model. Clinical validation was conducted on 34 patients having 2 consecutive MDCT
examinations within a minimum interval of 6 months. The AAA segmentation was performed twice by a experienced
radiologist (reference standard) and once by 3 unsupervised technologists on all 68 MDCT. The ICC for intra-observer
reproducibility was 0.992 (≥0.987) for D-max and 0.998 (≥0.994) for volume measurement. The ICC for inter-observer
reproducibility was 0.985 (0.977-0.90) for D-max and 0.998 (0.996- 0.999) for volume measurement. Semi-automated
AAA segmentation for volume follow-up was more than twice as sensitive than D-max follow-up, while providing an
equivalent reproducibility.

Coronary CT angiography (cCTA) has been reported to be an effective means for diagnosis of coronary artery disease.
We are investigating the feasibility of developing a computer-aided detection (CADe) system to assist radiologists in
detection of non-calcified plaques in coronary arteries in ECG-gated cCTA scans. In this study, we developed a
prototype vessel segmentation and tracking method to extract the coronary arterial trees which will define the search
space for plaque detection. Vascular structures are first enhanced by 3D multi-scale filtering and analysis of the
eigenvalues of Hessian matrices using a vessel enhancement response function specifically designed for coronary
arteries. The enhanced vascular structures are then segmented by an EM estimation method. The segmented coronary
arteries are tracked using a 3D dynamic balloon tracking (DBT) method. For this preliminary study, two starting seed
points were manually identified at the origins of the left and right coronary artery (LCA and RCA). The DBT method
automatically moves a sphere along the vessel whose diameter is adjusted dynamically based on the local vessel size,
tracks the vessels, and identifies its branches to generate the left and right coronary arterial trees. The algorithm was
applied to 20 cCTA scans that contained various degrees of coronary artery diseases. To evaluate the performance of
vessel segmentation and tracking, the rendered volume of coronary arteries tracked by our algorithm was displayed on
a PC, placed next to a GE Advantage workstation on which the coronary arterial trees tracked by the GE software and
the original cCTA scan were displayed. Two experienced thoracic radiologists visually examined the coronary arteries
on the cCTA scan and the segmented vessels to count untracked false-negative (FN) segments and false positives
(FPs). The comparison was made by radiologists' visual judgment because the digital files for the segmented vessels
were not accessible on the commercial system. A total of 19 and 38 artery segments were identified to be FNs, and 23
FPs and 20 FPs were found in the coronary trees tracked by our algorithm and the GE software, respectively. The
preliminary results demonstrated the feasibility of our approach.

Despite general acceptance that a healthy lifestyle and the treatment of risk factors can prevent the development
of cardiovascular diseases (CVD), CVD are the most common cause of death in Europe and the United States.
It has been shown that abdominal aortic calcifications (AAC) correlate strongly with coronary artery calcifications.
Hence an early detection of aortic calcified plaques helps to predict the risk of related coronary diseases.
Also since two thirds of the adverse events have no prior symptoms, possibilities to screen for risk in low cost
imaging are important. To this end the Morphological Atherosclerotic Calcification Distribution (MACD) index
was developed.
In the following several potential severity scores relating to the geometrical outline of the calcified deposits
in the lumbar aortic region are introduced. Their individual as well as their combined predictive power is examined
and a combined marker, MACD, is constructed. This is done using a Cox regression analysis, also known as
survival analysis. Furthermore we show how a Cox regression yields MACD to be the most efficient marker. We
also demonstrate that MACD has a larger individual predictive power than any of the other individual imaging
markers described. Finally we present that the MACD index predicts cardiovascular death with a hazard ratio
of approximately four.

Computer assisted detection (CAD) of lymph node metastases may help reduce reading time and improve
interpretation of the large amount of image data in an MR-lymphography exam. We compared the influence
of using different segmentation methods on the performance of a CAD system for classification of normal and
metastasized lymph nodes. Our database consisted of T1 and T2*-weighted pelvic MR images of 603 lymph
nodes, enhanced by USPIO contrast medium. For each lymph node, one seed point was manually defined
Three automated segmentation methods were compared: 1. Confidence Connected segmentation, extended with
automated Bandwidth Factor selection; 2. Conventional Graph Cut segmentation; 3. Pseudo-segmentation by
selecting a sphere around the seed point. All lymph nodes were also manually segmented by a radiologist. The
resulting segmentations were used to calculate 2 features (mean T1 and T2* signal intensity). Linear discriminant
analysis was used for classification. The diagnostic accuracy (AUC at ROC-analysis) was: 0.95 (Confidence-
Connected); 0.95 (Graph-Cut); 0.85 (spheres); and 0.95 (manual segmentations). The CAD performance of both
the Confidence Connected and Graph Cut methods was as good as the manual segmentation. The substantially
lower performance of the sphere segmentations demonstrates the need for accurate segmentations, even in USPIOenhanced
images.

Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar
density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We
developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing
filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an
edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching
algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour
segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the
liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen
prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated
liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean
liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean
absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard"
manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference
(p(F≤f)=0.32), and required substantially less completion time. Our automated scheme provides an efficient and accurate
way of measuring liver volumes.

We present a novel non-invasive automatic method for the classification and grading of liver fibrosis from fMRI
maps based on hepatic hemodynamic changes. This method automatically creates a model for liver fibrosis
grading based on training datasets. Our supervised learning method evaluates hepatic hemodynamics from an
anatomical MRI image and three T2*-W fMRI signal intensity time-course scans acquired during the breathing
of air, air-carbon dioxide, and carbogen. It constructs a statistical model of liver fibrosis from these fMRI scans
using a binary-based one-against-all multi class Support Vector Machine (SVM) classifier. We evaluated the
resulting classification model with the leave-one out technique and compared it to both full multi-class SVM
and K-Nearest Neighbor (KNN) classifications. Our experimental study analyzed 57 slice sets from 13 mice, and
yielded a 98.2% separation accuracy between healthy and low grade fibrotic subjects, and an overall accuracy
of 84.2% for fibrosis grading. These results are better than the existing image-based methods which can only
discriminate between healthy and high grade fibrosis subjects. With appropriate extensions, our method may
be used for non-invasive classification and progression monitoring of liver fibrosis in human patients instead of
more invasive approaches, such as biopsy or contrast-enhanced imaging.

Central-chest lymph nodes play a vital role in lung-cancer staging. The three-dimensional (3D) definition of
lymph nodes from multidetector computed-tomography (MDCT) images, however, remains an open problem.
This is because of the limitations in the MDCT imaging of soft-tissue structures and the complicated phenomena
that influence the appearance of a lymph node in an MDCT image. In the past, we have made significant efforts
toward developing (1) live-wire-based segmentation methods for defining 2D and 3D chest structures and (2)
a computer-based system for automatic definition and interactive visualization of the Mountain central-chest
lymph-node stations. Based on these works, we propose new single-click and single-section live-wire methods
for segmenting central-chest lymph nodes. The single-click live wire only requires the user to select an object
pixel on one 2D MDCT section and is designed for typical lymph nodes. The single-section live wire requires
the user to process one selected 2D section using standard 2D live wire, but it is more robust. We applied
these methods to the segmentation of 20 lymph nodes from two human MDCT chest scans (10 per scan) drawn
from our ground-truth database. The single-click live wire segmented 75% of the selected nodes successfully
and reproducibly, while the success rate for the single-section live wire was 85%. We are able to segment the
remaining nodes, using our previously derived (but more interaction intense) 2D live-wire method incorporated
in our lymph-node analysis system. Both proposed methods are reliable and applicable to a wide range of
pulmonary lymph nodes.

Many malignant processes cause abdominal lymphadenopathy, and computed tomography (CT) has become the primary
modality for its detection. A lymph node is considered enlarged (swollen) if it is more than 1 centimeter in diameter.
Which lymph nodes are swollen depends on the type of disease and the body parts involved. Identifying their locations is
very important to determine the possible cause. In the current clinical workflow, the detection and diagnosis of enlarged
lymph nodes is usually performed manually by examining all slices of CT images, which can be error-prone and time
consuming. 3D blob enhancement filter is a usual way for computer-aided node detection. We proposed a new 3D blob
detector for automatic lymph node detection in contrast-enhanced abdominal CT images. Since lymph nodes are
usually next to blood vessels, abdominal blood vessels were first segmented as a reference to set the search region for
lymph nodes. Then a new detection response measure, blobness, is defined based on eigenvalues of the Hessian matrix
and the object scale in our new blob detector. Voxels with higher blobness were clustered as lymph node candidates.
Finally some prior anatomical knowledge was utilized for false positive reduction. We applied our method to 5 patients
and compared the results with the performance of the original blobness definition. Both methods achieved sensitivity of
83.3% but the false positive rates per patient were 14 and 26 for our method and the original method, respectively. Our
results indicated that computer-aided lymph node detection with this new blob detector may yield a high sensitivity and
a relatively low FP rate in abdominal CT.

Hypodense metastases are not always completely distinguishable from benign cysts in the liver using conventional
Computed Tomography (CT) imaging, since the two lesion types present with overlapping intensity distributions
due to similar composition as well as other factors including beam hardening and patient motion. This problem
is extremely challenging for small lesions with diameter less than 1 cm. To accurately characterize such lesions,
multiple follow-up CT scans or additional Positron Emission Tomography or Magnetic Resonance Imaging exam
are often conducted, and in some cases a biopsy may be required after the initial CT finding. Gemstone
Spectral Imaging (GSI) with fast kVp switching enables projection-based material decomposition, offering the
opportunity to discriminate tissue types based on their energy-sensitive material attenuation and density. GSI
can be used to obtain monochromatic images where beam hardening is reduced or eliminated and the images
come inherently pre-registered due to the fast kVp switching acquisition. We present a supervised learning
method for discriminating between cysts and hypodense liver metastases using these monochromatic images.
Intensity-based statistical features extracted from voxels inside the lesion are used to train optimal linear and
nonlinear classifiers. Our algorithm only requires a region of interest within the lesion in order to compute
relevant features and perform classification, thus eliminating the need for an accurate segmentation of the lesion.
We report classifier performance using M-fold cross-validation on a large lesion database with radiologist-provided
lesion location and labels as the reference standard. Our results demonstrate that (a) classification using a single
projection-based spectral CT image, i.e., a monochromatic image at a specified keV, outperforms classification
using an image-based dual energy CT pair, i.e., low and high kVp images derived from the same fast kVp
acquisition and (b) classification using monochromatic images can achieve very high accuracy in separating
benign liver cysts and metastases, especially for small lesions.

Experienced radiologists are in short supply, and are sometimes called upon to read many images in a short amount of
time. This leaves them with a limited amount of time to read images, and can lead to fatigue and stress which can be
sources of error, as they overlook subtle abnormalities that they otherwise might not miss. Another factor in error rates
is called satisfaction of search, where a radiologist misses a second (typically subtle) abnormality after finding the first.
These types of errors are due primarily to a lack of attention to an important region of the image during the search. In
this paper we discuss the use of eye tracker technology, in combination with image analysis and machine learning
techniques, to learn what types of features catch the eye experienced radiologists when reading chest x-rays for
diagnostic purposes, and to then use that information to produce saliency maps that predict what regions of each image
might be most interesting to radiologists. We found that, out of 13 popular features types that are widely extracted to
characterize images, 4 are particularly useful for this task: (1) Localized Edge Orientation Histograms (2) Haar
Wavelets, (3) Gabor Filters, and (4) Steerable Filters.

This study describes a system for interactive annotation of thoracic CT scans. Lung volumes in these scans are
segmented and subdivided into roughly spherical volumes of interest (VOIs) with homogeneous texture using a
clustering procedure. For each 3D VOI, 72 features are calculated. The observer inspects the scan to determine
which textures are present and annotates, with mouse clicks, several VOIs of each texture. Based on these
annotations, a k-nearest-neighbor classifier is trained, which classifies all remaining VOIs in the scan. The
algorithm then presents a slice with suggested annotations to the user, in which the user can correct mistakes.
The classifier is retrained, taking into account these new annotations, and the user is presented another slice
for correction. This process continues until at least 50% of all lung voxels in the scan have been classified. The
remaining VOIs are classified automatically. In this way, the entire lung volume is annotated. The system has
been applied to scans of patients with usual and non-specific interstitial pneumonia. The results of interactive
annotation are compared to a setup in which the user annotates all predefined VOIs manually. The interactive
system is 3.7 times as fast as complete manual annotation of VOIs and differences between the methods are similar
to interobserver variability. This is a first step towards precise volumetric quantitation of texture patterns in
thoracic CT in clinical research and in clinical practice.

The computer aided diagnosis (CAD) of abnormalities on chest radiographs is difficult due to the presence of overlapping normal anatomy. Suppression of the normal anatomy is expected to improve performance of a CAD system, but such a method has not yet been applied to the computer detection of interstitial abnormalities such as occur in tuberculosis (TB). The aim of this research is to evaluate the effect of rib suppression on a CAD system for TB. Profiles of pixel intensities sampled perpendicular to segmented ribs were used to create a local PCA-based shape model of the rib. The model was normalized to the local background intensity and corrected for gradients perpendicular to the rib. Subsequently rib suppressed images were created by subtracting the models for each rib from the original image. The effect of rib suppression was evaluated using a CAD system for TB detection. Small square image patches were sampled randomly from 15 normal and 35 TB-affected images containing textural abnormalities. Abnormalities were outlined by a radiologist and were given a subtlety rating from 1 to 5. Features based on moments of intensity distributions of Gaussian derivative filtered images were extracted. A supervised learning approach was used to discriminate between normal and diseased image patches. The use of rib suppressed images increased the overall performance of the system, as measured by the area under the receiver operator characteristic (ROC) curve, from 0.75 to 0.78. For the more subtly rated patches (rated 1-3) the performance increased from 0.62 to 0.70.

A novel approach to feature optimization for classification of lung carcinoma using tissue images is presented. The
methodology uses a combination of three characteristics of computational features: F-measure, which is a representation
of each feature towards classification, inter-correlation between features and pathology based information. The metadata
provided from pathological parameters is used for mapping between computational features and biological information.
Multiple regression analysis maps each category of features based on how pathology information is correlated with the
size and location of cancer. Relatively the computational features represented the tumor size better than the location of
the cancer. Based on the three criteria associated with the features, three sets of feature subsets with individual validation
are evaluated to select the optimum feature subset. Based on the results from the three stages, the knowledgebase
produces the best subset of features. An improvement of 5.5% was observed for normal Vs all abnormal cases with Az
value of 0.731 and 74/114 correctly classified. The best Az value of 0.804 with 66/84 correct classification and
improvement of 21.6% was observed for normal Vs adenocarcinoma.

Topological texture features were compared in their ability to classify morphological patterns known as 'honeycombing'
that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution
computed tomography (HRCT) images. For 14 patients with known occurrence of honey-combing, a stack of 70
axial, lung kernel reconstructed images were acquired from HRCT chest exams. A set of 241 regions of interest
of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features
were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions
(MDs), and three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN) classifier
and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each
texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure
of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions
and the significance thresholds were adjusted for multiple comparisons by the Bonferroni correction.
The best classification results were obtained by the MF features, which performed significantly better than all
the standard GLCM and MD features (p < 0.005) for both classifiers. The highest accuracy was found for
MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively). The best standard texture features
were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate
that advanced topological texture features can provide superior classification performance in computer-assisted
diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

Radiological bone age assessment is based on local image regions of interest (ROI), such as the epiphysis or the area of
carpal bones. These are compared to a standardized reference and scores determining the skeletal maturity are calculated.
For computer-aided diagnosis, automatic ROI extraction and analysis is done so far mainly by heuristic approaches. Due
to high variations in the imaged biological material and differences in age, gender and ethnic origin, automatic analysis is
difficult and frequently requires manual interactions. On the contrary, epiphyseal regions (eROIs) can be compared to
previous cases with known age by content-based image retrieval (CBIR). This requires a sufficient number of cases with
reliable positioning of the eROI centers. In this first approach to bone age assessment by CBIR, we conduct leaving-oneout
experiments on 1,102 left hand radiographs and 15,428 metacarpal and phalangeal eROIs from the USC hand atlas.
The similarity of the eROIs is assessed by cross-correlation of 16x16 scaled eROIs. The effects of the number of eROIs,
two age computation methods as well as the number of considered CBIR references are analyzed. The best results yield
an error rate of 1.16 years and a standard deviation of 0.85 years. As the appearance of the hand varies naturally by up to
two years, these results clearly demonstrate the applicability of the CBIR approach for bone age estimation.

This work describes a method that can discriminate between a solid pulmonary nodule and a pulmonary vessel
bifurcation point at a given candidate location on a CT scan using the method of standard moments. The
algorithm starts with the estimation of a spherical window around a nodule candidate center that best captures
the local shape properties of the region. Then, given this window, the standard set of moments, invariant to
rotation and scale is computed over the geometric representation of the region. Finally, a feature vector composed
of the moment values is classified as either a nodule or a vessel bifurcation point.
The performance of this technique was evaluated on a dataset containing 276 intraparenchymal nodules and
276 selected vessel bifurcation points. The method resulted in 99% sensitivity and 80% specificity in identifying
nodules, which makes this technique an efficient filter for false positives reduction. Its efficiency was further
evaluated on the dataset of 656 low-dose chest CT scans. Inclusion of this filter into a design of an experimental
detection system resulted in up to a 69% decrease in false positive rate in detection of intraparenchymal nodules
with less than 1% loss in sensitivity.

With the advent of high-resolution CT, three-dimensional (3D) methods for nodule volumetry have been introduced,
with the hope that such methods will be more accurate and consistent than currently used planar measures of size.
However, the error associated with volume estimation methods still needs to be quantified. Volume estimation error is
multi-faceted in the sense that there is variability associated with the patient, the software tool and the CT system. A
primary goal of our current research efforts is to quantify the various sources of measurement error and, when possible,
minimize their effects. In order to assess the bias of an estimate, the actual value, or "truth," must be known. In this
work we investigate the reliability of micro CT to determine the "true" volume of synthetic nodules. The advantage of
micro CT over other truthing methods is that it can provide both absolute volume and shape information in a single
measurement. In the current study we compare micro CT volume truth to weight-density truth for spherical, elliptical,
spiculated and lobulated nodules with diameters from 5 to 40 mm, and densities of -630 and +100 HU. The percent
differences between micro CT and weight-density volume for -630 HU nodules range from [-21.7%, -0.6%] (mean=
-11.9%) and the differences for +100 HU nodules range from [-0.9%, 3.0%] (mean=1.7%).

We have previously presented a match filtered (MF) approach for estimating lung nodule size from helical
multi-detector CT (MDCT) images [1], in which we minimized the sum of squared differences between the
simulated CT templates and the actual nodule CT images. The previous study showed the potential of this
approach for reducing the bias and variance in nodule size estimation. However, minimizing SSD is not
statistically optimal because the noise in 3D helical CT images is correlated. The goal of this work is to
investigate the noise properties and explore several approximate descriptions of the three-dimensional (3D)
noise covariance for more accurate estimates. The approximations include: variance only, noise power
spectrum (NPS), axial correlation, two-dimensional (2D) in-plane correlation and fully 3D correlation. We
examine the effectiveness of these second-order noise approximations by applying them to our volume
estimation approach with a simulation study. Our simulations show that: the variance-based pre-whitening
and axial pre-whitening perform very similar to the non-prewhitening case, with accuracy (measured in
RMSE) differences within 1%; the NPS based pre-whitening performs slightly better, with a 4% decrease in
RMSE; the in-plane pre-whitening and 3D fully pre-whitening perform best, with about a 10% decrease in
RMSE over the non-prewhitening case. The simulation results suggest that the NPS, 2D in-plane and fully
3D prewhitening can be beneficial for lung nodule size estimation, albeit with greater computational costs in
determining these noise characterizations.

The segmentation of medical images is challenging because a ground truth is often not available. Computer-Aided
Detection (CAD) systems are dependent on ground truth as a means of comparison; however, in many cases the
ground truth is derived from only experts' opinions. When the experts disagree, it becomes impossible to discern
one ground truth. In this paper, we propose an algorithm to measure the disagreement among radiologist's
delineated boundaries. The algorithm accounts for both the overlap and shape of the boundaries in determining
the variability of a panel segmentation. After calculating the variability of 3788 thoracic computed tomography
(CT) slices in the Lung Image Database Consortium (LIDC), we found that the radiologists have a high consensus
in a majority of lung nodule segmentations. However, our algorithm identified a number of segmentations that
the radiologists significantly disagreed on. Our proposed method of measuring disagreement can assist others
in determining the reliability of panel segmentations. We also demonstrate that it is superior to simply using
overlap, which is currently one of the most common ways of measuring segmentation agreement. The variability
metric presented has applications to panel segmentations, and also has potential uses in CAD systems.

As part of a more general effort to probe the interrelated factors impacting the accuracy
and precision of lung nodule size estimation, we have been conducting phantom CT
studies with an anthropomorphic thoracic phantom containing a vasculature insert on
which synthetic nodules were inserted or attached. The utilization of synthetic nodules
with known truth regarding size and location allows for bias and variance analysis,
enabled by the acquisition of repeat CT scans. Using a factorial approach to probe
imaging parameters (acquisition and reconstruction) and nodule characteristics (size,
density, shape, location), ten repeat scans have been collected for each protocol and
nodule layout. The resulting database of CT scans is incrementally becoming available to
the public via the National Biomedical Imaging Archive to facilitate the assessment of
lung nodule size estimation methodologies and the development of image analysis
software among other possible applications. This manuscript describes the phantom CT
scan database and associated information including image acquisition and reconstruction
protocols, nodule layouts and nodule truth.

This work presents a novel approach for model based segmentation of the kidney in images acquired by Computed
Tomography (CT). The developed computer aided segmentation system is expected to support computer aided
diagnosis and operation planning. We have developed a deformable model based approach based on local shape
constraints that prevents the model from deforming into neighboring structures while allowing the global shape to
adapt freely to the data. Those local constraints are derived from the anatomical structure of the kidney and the
presence and appearance of neighboring organs. The adaptation process is guided by a rule-based deformation
logic in order to improve the robustness of the segmentation in areas of diffuse organ boundaries. Our work
flow consists of two steps: 1.) a user guided positioning and 2.) an automatic model adaptation using affine
and free form deformation in order to robustly extract the kidney. In cases which show pronounced pathologies,
the system also offers real time mesh editing tools for a quick refinement of the segmentation result. Evaluation
results based on 30 clinical cases using CT data sets show an average dice correlation coefficient of 93% compared
to the ground truth. The results are therefore in most cases comparable to manual delineation. Computation
times of the automatic adaptation step are lower than 6 seconds which makes the proposed system suitable for
an application in clinical practice.

Intervertebral disc herniation is a major reason for lower back pain (LBP), which is the second most common
neurological ailment in the United States. Automation of herniated disc diagnosis reduces the large burden
on radiologists who have to diagnose hundreds of cases each day using clinical MRI. We present a method
for automatic diagnosis of lumbar disc herniation using appearance and shape features. We jointly use the
intensity signal for modeling the appearance of herniated disc and the active shape model for modeling the
shape of herniated disc. We utilize a Gibbs distribution for classification of discs using appearance and shape
features. We use 33 clinical MRI cases of the lumbar area for training and testing both appearance and shape
models. We achieve over 91% accuracy in detection of herniation in a cross-validation experiment with specificity
of 91% and sensitivity of 94%.

In building robust classifiers for computer-aided detection (CAD) of lesions, selection of relevant features is of
fundamental importance. Typically one is interested in determining which, of a large number of potentially
redundant or noisy features, are most discriminative for classification. Searching all possible subsets of features
is impractical computationally. This paper proposes a feature selection scheme combining AdaBoost with the
Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative features. A fitness
function is designed to determine the optimal number of features in a forward wrapper search. Bagging is
applied to reduce the variance of the classifier and make a reliable selection. Experiments demonstrate that by
selecting just 11 percent of the total features, the classifier can achieve better prediction on independent test
data compared to the 70 percent of the total features selected by AdaBoost.

Computer-aided diagnosis (CAD) systems are indispensable tools for patients' healthcare in modern medicine.
Nevertheless, the only fully automatic CAD system available for lumbar stenosis today is for X-ray images. Its
performance is limited due to the limitations intrinsic to X-ray images. In this paper, we present a system for
magnetic resonance images. It employs a machine learning classification technique to automatically recognize
lumbar spine components. Features can then be extracted from these spinal components. Finally, diagnosis is done
by applying a Multilayer Perceptron. This classification framework can learn the features of different spinal
conditions from the training images. The trained Perceptron can then be applied to diagnose new cases for various
spinal conditions. Our experimental studies based on 62 subjects indicate that the proposed system is reliable and
significantly better than our older system for X-ray images.

We are developing a computer-aided detection (CAD) system for clustered microcalcifications in digital breast
tomosynthesis (DBT). In this preliminary study, we investigated the approach of detecting microcalcifications in the
tomosynthesized volume. The DBT volume is first enhanced by 3D multi-scale filtering and analysis of the eigenvalues
of Hessian matrices with a calcification response function and signal-to-noise ratio enhancement filtering. Potential
signal sites are identified in the enhanced volume and local analysis is performed to further characterize each object. A
3D dynamic clustering procedure is designed to locate potential clusters using hierarchical criteria. We collected a pilot
data set of two-view DBT mammograms of 39 breasts containing microcalcification clusters (17 malignant, 22 benign)
with IRB approval. A total of 74 clusters were identified by an experienced radiologist in the 78 DBT views. Our
prototype CAD system achieved view-based sensitivity of 90% and 80% at an average FP rate of 7.3 and 2.0 clusters per
volume, respectively. At the same levels of case-based sensitivity, the FP rates were 3.6 and 1.3 clusters per volume,
respectively. For the subset of malignant clusters, the view-based detection sensitivity was 94% and 82% at an average
FP rate of 6.0 and 1.5 FP clusters per volume, respectively. At the same levels of case-based sensitivity, the FP rates
were 1.2 and 0.9 clusters per volume, respectively. This study demonstrated that computerized microcalcification
detection in 3D is a promising approach to the development of a CAD system for DBT. Study is underway to further
improve the computer-vision methods and to optimize the processing parameters using a larger data set.

We present a novel method for the detection and reconstruction in 3D of microcalcifications in digital breast
tomosynthesis (DBT) image sets. From a list of microcalcification candidate regions (that is, real microcalcification
points or noise points) found in each DBT projection, our method: (1) finds the set of corresponding points of a
microcalcification in all the other projections; (2) locates its 3D position in the breast; (3) highlights noise points; and (4)
identifies the failure of microcalcification detection in one or more projections, in which case the method predicts the
image locations of the microcalcification in the images in which they are missed.
From the geometry of the DBT acquisition system, an "epipolar curve" is derived for the 2D positions a
microcalcification in each projection generated at different angular positions. Each epipolar curve represents a single
microcalcification point in the breast. By examining the n projections of m microcalcifications in DBT, one expects
ideally m epipolar curves each comprising n points. Since each microcalcification point is at a different 3D position,
each epipolar curve will be at a different position in the same 2D coordinate system. By plotting all the
microcalcification candidates in the same 2D plane simultaneously, one can easily extract a representation of the number
of microcalcification points in the breast (number of epipolar curves) and their 3D positions, the noise points detected
(isolated points not forming any epipolar curve) and microcalcification points missed in some projections (epipolar
curves with less than n points).

We are developing a computer-aided detection (CAD) system to assist radiologists in detecting microcalcification
clusters in digital breast tomosynthesis (DBT). The purpose of this study is to investigate the feasibility of a 2D approach
using the projection-view (PV) images as input. In the first stage, automated detection of the microcalcification clusters
on the PVs is performed. In the second stage, the detected cluster candidates or the individual microcalcifications on the
PVs are back-projected to the 3D volume. The true clusters or microcalcifications will therefore converge at their focal
planes and ideally will result in higher cluster or microcalcification scores than the FPs. In the final step an analysis of
the back-projected cluster or microcalcification candidates is performed to differentiate the true and false clusters. In this
pilot study, a limited data set of 39 cases with biopsy proven microcalcification clusters (17 malignant, 22 benign) was
used. The DBT scans were obtained in both CC and MLO views using a GE GEN2 prototype system which acquires 21
PVs over a 60º arc in 3º increments. In the 78 DBT volumes, a total of 74 clusters (33 malignant clusters in 34 breasts
and 41 benign clusters in 44 breasts) were identified by an experienced radiologist. The computer detected 61%
(956/1554) of the clusters on the PVs from the 74 scans. After back-projection of the microcalcification candidates detected
on the individual PVs and excluding the first few PVs that had higher noise in back-projection stage, 84% (62/74) of the true
clusters were detected in the 3D volume. Study is underway to develop methods to reduce FPs and to compare this 2D
approach with 3D or combined 2D and 3D approaches.

Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were used to classify the character of
suspicious breast lesions as benign or malignant on dynamic contrast-enhanced MRI studies. Lesions were identified and
annotated by an experienced radiologist on 54 MRI exams of female patients where histopathological reports were
available prior to this investigation. GLCMs were then extracted from these 2D regions of interest (ROI) for four
principal directions (0°, 45°, 90° & 135°) and used to compute Haralick texture features. A fuzzy k-nearest neighbor (k-
NN) classifier was optimized in ten-fold cross-validation for each texture feature and the classification performance was
calculated on an independent test set as a function of area under the ROC curve. The lesion ROIs were characterized by
texture feature vectors containing the Haralick feature values computed from each directional-GLCM; and the classifier
results obtained were compared to a previously used approach where the directional-GLCMs were summed to a nondirectional
GLCM which could further yield a set of texture feature values. The impact of varying the inter-pixel
distance while generating the GLCMs on the classifier's performance was also investigated. Classifier's AUC was found
to significantly increase when the high-dimensional texture feature vector approach was pursued, and when features
derived from GLCMs generated using different inter-pixel distances were incorporated into the classification task. These
results indicate that lesion character classification accuracy could be improved by retaining the texture features derived
from the different directional GLCMs rather than combining these to yield a set of scalar feature values instead.

Dynamic contrast enhanced Breast MRI (DCE BMRI) has emerged as powerful tool in the diagnostic work-up of breast
cancer. While DCE BMRI is very sensitive, specificity remains to be an issue. Consequently, there is a need for features
that support the classification of enhancing lesions into benign and malignant lesions. Traditional features include the
morphology and the texture of a lesion, as well as the kinetic parameters of the time-intensity curves, i.e., the temporal
change of image intensity at a given location. The kinetic parameters include initial contrast uptake of a lesion and the
type of the kinetic curve. The curve type is usually assigned to one of three classes: persistent enhancement (Type I),
plateau (Type II), and washout (Type III). While these curve types show a correlation with the tumor type (benign or
malignant), only a small sub-volume of the lesion is taken into consideration and the curve type will depend on the
location of the ROI that was used to generate the kinetic curve. Furthermore, it has been shown that the curve type
significantly depends on which MR scanner was used as well as on the scan parameters.
Recently, it was shown that the heterogeneity of a given lesion with respect to spatial variation of the kinetic curve type
is a clinically significant indicator for malignancy of a tumor. In this work we compare four quantitative measures for the
degree of heterogeneity of the signal enhancement ratio in a tumor and evaluate their ability of predicting the dignity of a
tumor. All features are shown to have an area under the ROC curve of between 0.63 and 0.78 (for a single feature).

Previous research has shown that a fuzzy C-means (FCM) approach to computerized lesion analysis has
the potential to aid radiologists in the interpretation of dynamic contrast-enhanced MRI (DCE-MRI) breast
exams. 1, 2 Our purpose in this study was to optimize the performance of the FCM approach with respect
to binary (benign/malignant) breast lesion classification in DCE-MRI. We used both raw (calculated from
kinetic data points) and empirically fitted3 kinetic features for this study. FCM was used to automatically
select a characteristic kinetic curve (CKC) based on intensity-time point data of voxels within each lesion,
using four different kinetic criteria: (1) maximum initial enhancement, (2) minimum shape index, (3) maximum
washout, and (4) minimum time to peak. We extracted kinetic features from these CKCs, which were
merged using linear discriminant analysis (LDA), and evaluated with receiver operating characteristic (ROC)
analysis. There was comparable performance for methods 1, 2, and 4, while method 3 was inferior. Next,
we modified use of the FCM method by calculating a feature vector for every voxel in each lesion and using
FCM to select a characteristic feature vector (CFV) for each lesion. Using this method, we achieved performance
similar to the four CKC methods. Finally, we generated lesion color maps using FCM membership
matrices, which facilitated the visualization of enhancing voxels in a given lesion.

Arterial spin labeling (ASL) is one of promising non-invasive magnetic resonance (MR) imaging techniques for
diagnosis of Alzheimer's disease (AD) by measuring cerebral blood flow (CBF). The aim of this study was to develop
a computer-aided classification system for AD patients based on CBFs measured by the ASL technique. The average
CBFs in cortical regions were determined as functional image features based on the CBF map image, which was
non-linearly transformed to a Talairach brain atlas by using a free-form deformation. An artificial neural network
(ANN) was trained with the CBF functional features in 10 cortical regions, and was employed for distinguishing patients
with AD from control subjects. For evaluation of the method, we applied the proposed method to 20 cases including
ten AD patients and ten control subjects, who were scanned a 3.0-Tesla MR unit. As a result, the area under the
receiver operating characteristic curve obtained by the proposed method was 0.893 based on a leave-one-out-by-case test
in identification of AD cases among 20 cases. The proposed method would be feasible for classification of patients
with AD.

In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain
atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy
detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal
anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under
consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other
reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal
region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio
within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain
atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method
has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.